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"... The Stanford typed dependencies representation was designed to provide a simple description of the grammatical relationships in a sentence that can easily be understood and effectively used by people without linguistic expertise who want to extract textual relations. In particular, rather than the p ..."

The Stanford typed dependencies representation was designed to provide a simple description of the grammatical relationships in a sentence that can easily be understood and effectively used by people without linguistic expertise who want to extract textual relations. In particular, rather than the phrase structure representations that have long dominated in the computational

"... User generated content is extremely valuable for mining market intelligence because it is unsolicited. We study the problem of analyzing users ’ sentiment and opinion in their blog, message board, etc. posts with respect to topics expressed as a search query. In the scenario we consider the matches ..."

User generated content is extremely valuable for mining market intelligence because it is unsolicited. We study the problem of analyzing users ’ sentiment and opinion in their blog, message board, etc. posts with respect to topics expressed as a search query. In the scenario we consider the matches of the search query terms are expanded through coreference and meronymy to produce a set of mentions. The mentions are contextually evaluated for sentiment and their scores are aggregated (using a data structure we introduce call the sentiment propagation graph) to produce an aggregate score for the input entity. An extremely crucial part in the contextual evaluation of individual mentions is finding which sentiment expressions are semantically related to (target) which mentions — this is the focus of our paper. We present an approach where potential target mentions for a sentiment expression are ranked using supervised machine learning (Support Vector Machines) where the main features are the syntactic configurations (typed dependency paths) connecting the sentiment expression and the mention. We have created a large English corpus of product discussions blogs annotated with semantic types of mentions, coreference, meronymy and sentiment targets. The corpus proves that coreference and meronymy are not marginal phenomena but are really central to determining the overall sentiment for the toplevel entity. We evaluate a number of techniques for sentiment targeting and present results which we believe push the current state-of-the-art. 1.

"... The paper describes a novel rule-based approach to classification of opinion statements on the level of individual sentences. In contrast to existing approaches, the proposed method relies on the rules elaborated for semantically distinct verb classes. To deeply analyse the type, strength, and confi ..."

The paper describes a novel rule-based approach to classification of opinion statements on the level of individual sentences. In contrast to existing approaches, the proposed method relies on the rules elaborated for semantically distinct verb classes. To deeply analyse the type, strength, and confidence level of expressed opinion, the system relies on the compositionality principle and lexicon of sentiment-conveying terms, functional words, modifiers, and modal expressions. The method is capable of processing sentences of different complexity, including simple, compound, complex (with complement and relative clauses), and complex-compound sentences.

"... Re-distributed by Stanford University under license with the author. This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License. ii I certify that I have read this dissertation and that, in my opinion, it is fully adequate ..."

Re-distributed by Stanford University under license with the author. This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License. ii I certify that I have read this dissertation and that, in my opinion, it is fully adequate